Proactively identify, engage and track households at risk of homelessness

The Homelessness Reduction Act is expected to come into force in early 2018. The Act places a new duty on local authorities to prevent the homelessness of all families and single people who are eligible for assistance and threatened with homelessness, regardless of priority need.

This new duty is challenging for councils because people can become homeless for many reasons. The risk of homelessness is affected by a person’s response to structural, social and economic factors outside of their control.

Working with a number of funded trail blazing councils, Policy in Practice has identified many of these structural pressures through household level data, which means it is possible to predict and identify those who may be at risk.

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Our Low Income Family Tracker (LIFT) dashboard uses councils’ administrative data to identify households at risk of homelessness, predict demand and track the effectiveness of interventions designed to prevent homelessness. Our software helps frontline advisors engage residents and provides personalised actions to help people sustain tenancies.

Councils already have the data they need to help prevent future homelessness. Our predictive approach helps councils to forecast resources required to support people at risk and meet their homelessness duty.

Our offer

  1. LIFT dashboard linking multiple datasets
  2. Benefit and Budgeting Calculator software
  3. Policy impact analysis with action plan
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Tackle 3 key homelessness prevention challenges

1. Identify who’s at risk

We identify the pathways into homelessness and assess risk, based on financial resilience.

Using your Housing Benefit and Council Tax support data we identify predictors of homelessness in your area.

We segment your households based on their current circumstances and future risk of homelessness. They’re grouped as coping, struggling, at risk of homelessness or in crisis.

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2. Engage those at risk

Some vulnerable households may not already be known to you. We help you identify and then engage with people through targeted and personalised communications.

You can focus support where it’s needed.

This means you can help people toward greater financial independence through consistently effective frontline support, to help reduce the risk of homelessness.

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3. Track what happens

Councils want to better understand the vulnerabilities of low income households.

We track the changing circumstances of households, such as change in tenure. The systematic and scalable approach uses smart analysis of administrative data and vulnerability markers.

The cost of moving from secure housing to being becoming homeless can be told by analysis of historical datasets.

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Case study: tackling homelessness in London

Policy in Practice is working with Trust for London to pool together Housing Benefit and Council Tax Support data across 19 London Boroughs. This project tracks employment, income and housing circumstances of over 570,000 low-income Londoners over 19 months.

London, more than any other region, is affected by the sustained housing crisis in the UK. Over 70% of all families who are housed in temporary and emergency accommodation in the UK are in London.

For Londoners on low incomes, sustaining their tenancy is a serious and constant concern.  For local authorities in the capital, the rise in homelessness is a key challenge.

Our project looks to tackle this issue head on by exploring how household-level data can be used to predict demand for homelessness and temporary accommodation, helping London boroughs to take preventative action.

Initial findings from the analysis revealed that lone parents are more likely to end up in temporary housing, and that boroughs with the highest historical increase in private sector rents are now hosting a greater proportion of homeless households.

With this knowledge we added twelve other potential predictors of homelessness, including:

  • changes in employment and family circumstances
  • financial resilience
  • information on Council Tax and rent arrears and
  • discretionary support funds.

Adding these extra predictors has let us develop a comprehensive model to accurately predict the risk of homelessness among low-income households in London.

The dataset is being expanded to include Citizens Index-type databases held by some local councils. Easily integrated, they can increase the predictive capability of our model.

Following on from our pan-London analysis, we are working with eight councils across the UK to expand the scope of our model. We are developing and iteratively improving a robust, predictive approach to identify and engage residents, tackling homelessness upstream.

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